A minimum entropy approach to rule learning from examples

Learning from examples uses specific instances (examples and counterexamples) to produce general rules. It is a convenient learning scheme in cases where the process of interviewing human experts and analyzing and formalizing their decision is very difficult or time consuming. The system proposed is capable of obtaining the rules that fit a set of examples and counterexamples based on the minimal entropy (ME) criterion. The system proposed can also set various parameters of the rule (e.g., thresholds) in such a way that entropy is minimized. The system can also handle incremental learning from examples. Applications of the proposed system to seismic image analysis are included. >

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